import streamlit as st from openai import OpenAI from app_config import * from app_access_db import * model = "gpt-3.5-turbo" # ------------------------------------------------------------------------------------------------ # SIDEBAR # ------------------------------------------------------------------------------------------------ st.sidebar.title('OpenAI Business Chat') st.sidebar.write('This chat bot is build with Tools and Function feature of OpenAI to be able to answer question regarding applications and performance of officers') st.sidebar.markdown(""" ### Having a sample database with a structure - application - app_number - amount - amount_paid - state. (APPROVED, REJECTED, PENDING_PAYMENT, PAID) - office_code [FK] - service_code [FK] - date_created - date_paid - date_processed - office - office_name - office_location_code [FK] - location - location_name - location_code - service - service_code - service_name ### The chatbot will provide answers from that database - The number of applications rejected is a location during the current month - The trend of applications in particular states, for a location - Any question you think relevant from this DB """) def onchange_openai_key(): print(st.session_state.openai_key) openai_key = st.sidebar.text_input('OpenAI key', on_change=onchange_openai_key, key='openai_key') def submit_openai_key(model=model): if(openai_key == None or openai_key==''): st.sidebar.write('Please provide the key before') return else: client = OpenAI(api_key=openai_key) model = model completion = client.chat.completions.create( model=model, messages=[ {"role": "system", "content": "You are an assistant giving simple and short answer for question of child"}, {"role": "user", "content": "count from 0 to 10"} ] ) st.sidebar.write(f'Simple count : {completion.choices[0].message.content}') submit_key = st.sidebar.button(label='Submit', on_click=submit_openai_key) # ------------------------------------------------------------------------------------------------ # CHAT # ------------------------------------------------------------------------------------------------ st.title('OpenAI Business Chat') st.write(f'Ask any question that can be answer by the LLM {model}.') def askQuestion(model=model, question=''): if(openai_key == None or openai_key==''): print('Please provide the key before') return 'LLM API is not defined. Please provide the key before' else: client = OpenAI(api_key=openai_key) model = model completion = client.chat.completions.create( model=model, messages=[ {"role": "system", "content": f'{query_context}'}, {"role": "user", "content": f'{question}'} ] ) return completion.choices[0].message.content class AssistantMessage: def __init__(self): self.sql : str self.response_data : DataFrame def displayAssistantMessage( assistantMessage: AssistantMessage): with st.chat_message("assistant"): st.code(assistantMessage.sql, language='sql') st.code(assistantMessage.response_data, language='markdown') if assistantMessage.response_data.columns.size == 2: st.bar_chart(assistantMessage.response_data, x=assistantMessage.response_data.columns[0], y=assistantMessage.response_data.columns[1]) if assistantMessage.response_data.columns.size == 1: st.metric(label=assistantMessage.response_data.columns[0], value=f'{assistantMessage.response_data.values[0]}') # Initialize chat history if "messages" not in st.session_state: st.session_state.messages = [] # Display chat messages from history on app rerun for message in st.session_state.messages: if message["role"] == "user": with st.chat_message(message["role"]): st.markdown(message["content"]) elif message["role"] == "assistant": displayAssistantMessage(message["content"]) # React to user input if prompt := st.chat_input("What is up?"): with st.status('Running', expanded=True) as status: # Display user message in chat message container st.chat_message("user").markdown(prompt) # Add user message to chat history st.session_state.messages.append({"role": "user", "content": prompt}) response = askQuestion(question=prompt) # st.code(response, language='sql') response_data = run_query(response) # Display assistant response in chat message container assistanMsg = AssistantMessage() assistanMsg.sql = response assistanMsg.response_data = response_data displayAssistantMessage(assistanMsg) # with st.chat_message("assistant"): # st.code(response, language='sql') # st.caption(response_data) # st.bar_chart(response_data, x=response_data.columns[0], y=response_data.columns[1]) # Add assistant response to chat history st.session_state.messages.append({"role": "assistant", "content": assistanMsg}) status.update(label='Response of last question', state="complete", expanded=True)